The first group of methods aims to translate an image from source to target domain. In [1]}, authors used CycleGAN for unpaired image-to-image translation. Further, this method was implemented both for the MRI [2]} and CT [3]} images translation. The paired image-to-image translation is closer to our setup. Such a method requires the image pairs that have different style but the same semantic content. CT images that differs only in reconstruction kernel correspond to this setup. Here, the authors of [4]} and [5]} proposed a convolutional neural network (CNN) to translate the images reconstructed with one kernel to another kernel. Despite the reasonable performance, image translation methods lack the generalization ability. If the data consists of more than two domains, we need to train a separate model for every pair. They also do not address the cases with the unseen domains. Therefore, we leave the image translation approaches without consideration.
